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All components in LL subband are selected as non-zero components without testing for the correlation.
All elements in the LL3 subband are selected as non-zero elements without testing for the correlation.
They then test for jointness post hoc via hypothesis testing for the correlation parameter (rho) between the error terms of the simultaneous equations.
Specifically, these models involved hypothesis testing for the correlation coefficient when the independent variable was quantitative and one-factor ANOVA when it was qualitative.
For example, in the study by Le Gac et al. [ 30], assortative mating was evaluated by testing for the correlation between mating rates (intra- and interspecific) and the genetic distances between several Microbotryum species.
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The p values from the permutation test for the correlation between UNM_ICref_composite and composite cognitive scores are 0.02, 0.01, 0.001 for sMRI, dMRI, and fMRI, respectively (Fig. 2b).
The p values derived from a permutation test for the correlation (details provided in Supplementary Note 1 and Supplementary Fig. 1) between FBIRN_ICref_composite and composite cognitive scores are pperm = 1.3×10−4*, 0.002, 1.0×10−4* for sMRI, dMRI, and fMRI, respectively (Fig. 1b).
To detect significant similarity, we estimated two-sided P values by t-test for the correlation coefficient, and set α level at 0.05 with the Bonferroni correction dividing α by 4718.
A Pearson correlation coefficient and significance test for the correlation coefficient was calculated using SPSS 14.0.
We tested for the correlation of LP and eigenvectors using logistic regression since the response variable was dichotomous.
We used a linear model (LM) regression analysis to test for the correlation between traits in all sampled specimens.
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